We covered the entire offline evaluation pipeline: from generating a ground truth dataset to assessing performance with metrics like MRR, Hit Rate, and Cosine Similarity, plus the fascinating LLM-as-a-Judge approach.
#LLMZoomcamp #RAG #LLM #Evaluation
We covered the entire offline evaluation pipeline: from generating a ground truth dataset to assessing performance with metrics like MRR, Hit Rate, and Cosine Similarity, plus the fascinating LLM-as-a-Judge approach.
#LLMZoomcamp #RAG #LLM #Evaluation
Built with Google Gemini, LangChain, ChromaDB & Streamlit.
Let me share what I learned... 🧵
#LLMZOOMCAMP #BuildInPublic #AI
Built with Google Gemini, LangChain, ChromaDB & Streamlit.
Let me share what I learned... 🧵
#LLMZOOMCAMP #BuildInPublic #AI
I can now have dinner table conversations on embedding, vector databases and vector searches. I dont mind the looks from family members. Week 3 Lets go
@qdrant.bsky.social
#llmzoomcamp
I can now have dinner table conversations on embedding, vector databases and vector searches. I dont mind the looks from family members. Week 3 Lets go
@qdrant.bsky.social
#llmzoomcamp
Built a complete RAG system with:
✅ Optimized retrieval (k=3, 86.67% precision)
✅ Evaluated prompts (8.0/10 quality)
✅ Real-time monitoring (7 charts)
✅ Full Docker deployment
✅ Hallucination prevention
#LLMZOOMCAMP #BuildInPublic
Built a complete RAG system with:
✅ Optimized retrieval (k=3, 86.67% precision)
✅ Evaluated prompts (8.0/10 quality)
✅ Real-time monitoring (7 charts)
✅ Full Docker deployment
✅ Hallucination prevention
#LLMZOOMCAMP #BuildInPublic
Next stop: Production-ready AI agents! 🚀
#LLMZoomcamp #AIAgents #MachineLearning
Next stop: Production-ready AI agents! 🚀
#LLMZoomcamp #AIAgents #MachineLearning
🔹 Installed and used dlt with Qdrant support
🔹 Loaded FAQ data into a Qdrant vector database
🔹 Created and ran a dlt pipeline for data ingestion
🔹 Explored embedding models used during data insertion
Hands-on, practical learning! 📊 #llmzoomcamp
🔹 Installed and used dlt with Qdrant support
🔹 Loaded FAQ data into a Qdrant vector database
🔹 Created and ran a dlt pipeline for data ingestion
🔹 Explored embedding models used during data insertion
Hands-on, practical learning! 📊 #llmzoomcamp
• LLM: Google Gemini 2.5 Pro
• Embeddings: text-embedding-004
• Vector DB: ChromaDB
• Framework: LangChain
• UI: Streamlit
• Container: Docker
All production-ready with monitoring!
#LLMZOOMCAMP #TechStack
• LLM: Google Gemini 2.5 Pro
• Embeddings: text-embedding-004
• Vector DB: ChromaDB
• Framework: LangChain
• UI: Streamlit
• Container: Docker
All production-ready with monitoring!
#LLMZOOMCAMP #TechStack
Tested with out-of-scope questions.
System correctly says "I cannot tell you based on the provided context" instead of making things up.
Honesty > Confidence
#LLMZOOMCAMP #AIEthics
Tested with out-of-scope questions.
System correctly says "I cannot tell you based on the provided context" instead of making things up.
Honesty > Confidence
#LLMZOOMCAMP #AIEthics
#LLMZoomcamp #DataTalksClub
#LLMZoomcamp #DataTalksClub